{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 现有一批描述家庭用电情况的数据，对数据进行算法模型预测，并最终得到预测模型（每天各个时间段和功率之间的关系、功率与电流之间的关系等） 建议：使用python的sklearn库的linear_model中LinearRegression来获取算法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import time\n",
    "import sklearn\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path='datas\\household_power_consumption_1000.txt'\n",
    "names=['Date','Time','Global_active_power','Global_reactive_power','Voltage','Global_intensity','Sub_metering_1','Sub_metering_2','Sub_metering_3']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#读取数据\n",
    "df = pd.read_csv(path,sep=';')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Global_active_power</th>\n",
       "      <th>Global_reactive_power</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>Global_intensity</th>\n",
       "      <th>Sub_metering_1</th>\n",
       "      <th>Sub_metering_2</th>\n",
       "      <th>Sub_metering_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16/12/2006</td>\n",
       "      <td>17:24:00</td>\n",
       "      <td>4.216</td>\n",
       "      <td>0.418</td>\n",
       "      <td>234.84</td>\n",
       "      <td>18.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16/12/2006</td>\n",
       "      <td>17:25:00</td>\n",
       "      <td>5.360</td>\n",
       "      <td>0.436</td>\n",
       "      <td>233.63</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16/12/2006</td>\n",
       "      <td>17:26:00</td>\n",
       "      <td>5.374</td>\n",
       "      <td>0.498</td>\n",
       "      <td>233.29</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16/12/2006</td>\n",
       "      <td>17:27:00</td>\n",
       "      <td>5.388</td>\n",
       "      <td>0.502</td>\n",
       "      <td>233.74</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16/12/2006</td>\n",
       "      <td>17:28:00</td>\n",
       "      <td>3.666</td>\n",
       "      <td>0.528</td>\n",
       "      <td>235.68</td>\n",
       "      <td>15.8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Time  Global_active_power  Global_reactive_power  Voltage  \\\n",
       "0  16/12/2006  17:24:00                4.216                  0.418   234.84   \n",
       "1  16/12/2006  17:25:00                5.360                  0.436   233.63   \n",
       "2  16/12/2006  17:26:00                5.374                  0.498   233.29   \n",
       "3  16/12/2006  17:27:00                5.388                  0.502   233.74   \n",
       "4  16/12/2006  17:28:00                3.666                  0.528   235.68   \n",
       "\n",
       "   Global_intensity  Sub_metering_1  Sub_metering_2  Sub_metering_3  \n",
       "0              18.4             0.0             1.0            17.0  \n",
       "1              23.0             0.0             1.0            16.0  \n",
       "2              23.0             0.0             2.0            17.0  \n",
       "3              23.0             0.0             1.0            17.0  \n",
       "4              15.8             0.0             1.0            17.0  "
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#打印了前五行数据\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#看所有的变量值\n",
    "# for i in df.columns:\n",
    "#     print df[i].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#空值的处理\n",
    "new_df = df.replace('?',np.nan)\n",
    "datas = new_df.dropna(how='any')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#创建一个时间字符串格式化\n",
    "def date_format(dt):\n",
    "    import time\n",
    "    t = time.strptime(' '.join(dt),'%d/%m/%Y %H:%M:%S')\n",
    "    return(t.tm_year,t.tm_mon,t.tm_mday,t.tm_hour,t.tm_min,t.tm_sec)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#获取X，Y变量，讲时间转换为数值型的连续变量\n",
    "X = datas[names[0:2]]\n",
    "X = X.apply(lambda x :pd.Series(date_format(x)),axis=1)\n",
    "Y = datas[names[2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      0   1   2   3   4  5\n",
      "0  2006  12  16  17  24  0\n",
      "1  2006  12  16  17  25  0\n",
      "2  2006  12  16  17  26  0\n",
      "3  2006  12  16  17  27  0\n",
      "4  2006  12  16  17  28  0\n",
      "0    4.216\n",
      "1    5.360\n",
      "2    5.374\n",
      "3    5.388\n",
      "4    3.666\n",
      "Name: Global_active_power, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(X.head(5))\n",
    "print(Y.head(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 对数据集进行训练集、测试集划分\n",
    "# random_state 是随机数的种子。其实就是该组随机数的编号，在需要重复试验的时候，保证得到一组一样的随机数。\n",
    "# 种子不同，产生不同的随机数；种子相同，即使实例不同也产生相同的随机数。\n",
    "X_train,X_test,Y_train,Y_test=train_test_split(X,Y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 标准化：将数据变换为均值为0、标准差为1的标准正态分布，使得预测结果不会被某些维度过大的特征值而主导\n",
    "# 标准化并不是为了方便与其他数据一同处理或比较，而是为数据的下一步处理做准备\n",
    "# 比如数据经过零-均值标准化后，更利于使用标准正态分布的性质\n",
    "ss=StandardScaler()\n",
    "# fit_transform()先训练拟合数据，再标准化\n",
    "X_train=ss.fit_transform(X_train)\n",
    "X_test = ss.transform(X_test)\n",
    "\n",
    "# PS: 归一化与标准化的区别：https://www.zhihu.com/question/20467170"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#训练模型\n",
    "lr = LinearRegression()\n",
    "lr.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率： 0.244093118059\n"
     ]
    }
   ],
   "source": [
    "#模型检验\n",
    "print(\"准确率：\",lr.score(X_train,Y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#预测y值\n",
    "y_predict = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.externals import joblib\n",
    "#模型保存\n",
    "joblib.dump(ss,\"data_ss.model\")\n",
    "joblib.dump(lr,\"data_lr.model\")\n",
    "#加载模型\n",
    "joblib.load(\"data_ss.model\")\n",
    "joblib.load(\"data_lr.model\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#设置字符集，防止中文乱码\n",
    "mpl.rcParams['font.sans-serif']=[u'simHei']\n",
    "mpl.rcParams['axes.unicode_minus']=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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8E19++SVWr17t8Lk8evQIb775JuLi4lCqVCn8/PPPdgfnOYtp1tSQf/75x+G6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\nFixYsHAGwCJ8CxYsWLBg4QzA/wNrmidCDIGl8QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcbe1748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#预测值与实际值画(图比较\n",
    "t = np.arange(len(X_test))\n",
    "plt.figure(facecolor='w')\n",
    "plt.plot(t,Y_test,'r-',linewidth=2,label=u'真实值')\n",
    "plt.plot(t,y_predict,'g-',linewidth=2,label=u'预测值')   \n",
    "plt.legend(loc='lower right')\n",
    "plt.title(u'线性回归预测时间与功率之间的关系',fontsize=20)\n",
    "plt.grid(b=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
